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Title: Trip Energy Estimation Methodology and Model Based on Real-World Driving Data for Green-Routing Applications

Abstract

A data-informed model to predict energy use for a proposed vehicle trip has been developed in this paper. The methodology leverages roughly one million miles of real-world driving data to generate the estimation model. Driving is categorized at the sub-trip level by average speed, road gradient, and road network geometry, then aggregated by category. An average energy consumption rate is determined for each category, creating an energy rate look-up table. Proposed vehicle trips are then categorized in the same manner, and estimated energy rates are appended from the look-up table. The methodology is robust and applicable to a wide range of driving data. The model has been trained on vehicle travel profiles from the Transportation Secure Data Center at the National Renewable Energy Laboratory and validated against on-road fuel consumption data from testing in Phoenix, Arizona. When compared against the detailed on-road conventional vehicle fuel consumption test data, the energy estimation model accurately predicted which route would consume less fuel over a dozen different tests. When compared against a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology should accurately select the route that consumes the least fuel 90% of the time. The model resultsmore » can be used to inform control strategies in routing tools, such as change in departure time, alternate routing, and alternate destinations to reduce energy consumption. This work provides a highly extensible framework that allows the model to be tuned to a specific driver or vehicle type.« less

Authors:
 [1];  [1];  [1];  [1];  [1];  [2]
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  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  2. Idaho National Lab. (INL), Idaho Falls, ID (United States)
Publication Date:
Research Org.:
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
OSTI Identifier:
1477948
Report Number(s):
NREL/JA-5400-71111
Journal ID: ISSN 0361-1981
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Accepted Manuscript
Journal Name:
Transportation Research Record
Additional Journal Information:
Journal Volume: 2672; Journal Issue: 24; Journal ID: ISSN 0361-1981
Publisher:
National Academy of Sciences, Engineering and Medicine
Country of Publication:
United States
Language:
English
Subject:
33 ADVANCED PROPULSION SYSTEMS; energy estimation; drive cycles; green routing; GPS trajectories; transportation data; trip energy

Citation Formats

Holden, Jacob, Van Til, Harrison, Wood, Eric, Zhu, Lei, Gonder, Jeffrey, and Shirk, Matthew. Trip Energy Estimation Methodology and Model Based on Real-World Driving Data for Green-Routing Applications. United States: N. p., 2018. Web. doi:10.1177/0361198118798286.
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Holden, Jacob, Van Til, Harrison, Wood, Eric, Zhu, Lei, Gonder, Jeffrey, & Shirk, Matthew. Trip Energy Estimation Methodology and Model Based on Real-World Driving Data for Green-Routing Applications. United States. https://doi.org/10.1177/0361198118798286
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Holden, Jacob, Van Til, Harrison, Wood, Eric, Zhu, Lei, Gonder, Jeffrey, and Shirk, Matthew. Fri . "Trip Energy Estimation Methodology and Model Based on Real-World Driving Data for Green-Routing Applications". United States. https://doi.org/10.1177/0361198118798286. https://www.osti.gov/servlets/purl/1477948.
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@article{osti_1477948,
title = {Trip Energy Estimation Methodology and Model Based on Real-World Driving Data for Green-Routing Applications},
author = {Holden, Jacob and Van Til, Harrison and Wood, Eric and Zhu, Lei and Gonder, Jeffrey and Shirk, Matthew},
abstractNote = {A data-informed model to predict energy use for a proposed vehicle trip has been developed in this paper. The methodology leverages roughly one million miles of real-world driving data to generate the estimation model. Driving is categorized at the sub-trip level by average speed, road gradient, and road network geometry, then aggregated by category. An average energy consumption rate is determined for each category, creating an energy rate look-up table. Proposed vehicle trips are then categorized in the same manner, and estimated energy rates are appended from the look-up table. The methodology is robust and applicable to a wide range of driving data. The model has been trained on vehicle travel profiles from the Transportation Secure Data Center at the National Renewable Energy Laboratory and validated against on-road fuel consumption data from testing in Phoenix, Arizona. When compared against the detailed on-road conventional vehicle fuel consumption test data, the energy estimation model accurately predicted which route would consume less fuel over a dozen different tests. When compared against a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology should accurately select the route that consumes the least fuel 90% of the time. The model results can be used to inform control strategies in routing tools, such as change in departure time, alternate routing, and alternate destinations to reduce energy consumption. This work provides a highly extensible framework that allows the model to be tuned to a specific driver or vehicle type.},
doi = {10.1177/0361198118798286},
journal = {Transportation Research Record},
number = 24,
volume = 2672,
place = {United States},
year = {Fri Sep 21 00:00:00 EDT 2018},
month = {Fri Sep 21 00:00:00 EDT 2018}
}
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Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1177/0361198118798286
Copyright Statement
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Citation Metrics:
Cited by: 4 works
Citation information provided by
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Figures / Tables:

Table 1 Table 1: Summary of the TSDC Drive Cycles Used to Inform the Energy Estimation Model
All figures and tables (6 total)
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Works referenced in this record:

Real-World Carbon Dioxide Impacts of Traffic Congestion
journal, January 2008

  • Barth, Matthew; Boriboonsomsin, Kanok
  • Transportation Research Record: Journal of the Transportation Research Board, Vol. 2058, Issue 1
  • DOI: 10.3141/2058-20

Trajectory Segmentation Map-Matching Approach for Large-Scale, High-Resolution GPS Data
journal, January 2017

  • Zhu, Lei; Holden, Jacob R.; Gonder, Jeffrey D.
  • Transportation Research Record: Journal of the Transportation Research Board, Vol. 2645, Issue 1
  • DOI: 10.3141/2645-08

Energy and Environmental Impacts of Roadway Grades
journal, January 2006

  • Park, Sangjun; Rakha, Hesham
  • Transportation Research Record: Journal of the Transportation Research Board, Vol. 1987, Issue 1
  • DOI: 10.1177/0361198106198700116

DOE SMART Mobility: Systems and Modeling for Accelerated Research in Transportation
book, January 2016

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